Automatic Text Classification via Supervised Learning
an S4 class containing the analytics for a classified set of documents...
an S4 class containing the analytics for a classified set of documents...
converts a tm DocumentTermMatrix or TermDocumentMatrix into a matrix.c...
makes predictions from a train_model() object.
makes predictions from a train_models() object.
creates an object of class analytics given classification results.
creates a container for training, classifying, and analyzing documents...
creates a summary with ensemble coverage and precision.
creates a document-term matrix to be passed into create_container().
creates a summary with precision, recall, and F1 scores.
creates a summary with the best label for each document.
used for cross-validation of various algorithms.
Query the languages supported in this package
an S4 class containing the training and classification matrices.
a sample dataset containing labeled headlines from The New York Times.
prints available algorithms for train_model() and train_models().
reads data from files into an R data frame.
calculates the recall accuracy of the classified data.
summarizes the analytics-class class
summarizes the analytics_virgin-class class
makes a model object using the specified algorithm.
makes a model object using the specified algorithms.
a sample dataset containing labeled bills from the United State Congre...
Get the common root/stem of words
A machine learning package for automatic text classification that makes it simple for novice users to get started with machine learning, while allowing experienced users to easily experiment with different settings and algorithm combinations. The package includes eight algorithms for ensemble classification (svm, slda, boosting, bagging, random forests, glmnet, decision trees, neural networks), comprehensive analytics, and thorough documentation.